MicroRNA (miRNA) is an important small RNA which regulates diverse gene expression at the post-transcriptional level. miRNAs are considered as important biomarkers since abnormal expression of specific miRNAs is associated with many diseases including cancer and diabetes. Therefore, it is important to develop biosensors to quantitatively detect miRNA expression levels. Here, we develop a nanosized graphene oxide (NGO) based miRNA sensor, which allows quantitative monitoring of target miRNA expression levels in living cells. The strategy is based on tight binding of NGO with peptide nucleic acid (PNA) probes, resulting in fluorescence quenching of the dye that is conjugated to the PNA, and subsequent recovery of the fluorescence upon addition of target miRNA. PNA as a probe for miRNA sensing offers many advantages including high sequence specificity, high loading capacity on the NGO surface compared to DNA and resistance against nuclease-mediated degradation. The present miRNA sensor allowed the detection of specific target miRNAs with the detection limit as low as ~1 pM and the simultaneous monitoring of three different miRNAs in a living cell.

Graphene oxide (GO) is one of the most attributed materials for opening new possibilities in the development of next generation biosensors. Due to the coexistence of hydrophobic domain from pristine graphite structure and hydrophilic oxygen containing functional groups, GO exhibits good water dispersibility, biocompatibility, and high affinity for specific biomolecules as well as properties of graphene itself partly depending on preparation methods. These properties of GO provided a lot of opportunities for the development of novel biological sensing platforms, including biosensors based on fluorescence resonance energy transfer (FRET), laser desorption/ionization mass spectrometry (LDI-MS), surface-enhanced Raman spectroscopy (SERS), and electrochemical detection. In this review, we classify GO-based biological sensors developed so far by their signal generation strategy and provide the comprehensive overview of them. In addition, we offer insights into how the GO attributed in each sensor system and how they improved the sensing performance.

Users of social media often share their feelings or emotional states through their posts. in this study, we developed a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from mental health communities in Reddit. By analyzing and learning posting information written by users, our proposed model could accurately identify whether a user's post belongs to a specific mental disorder, including depression, anxiety, bipolar, borderline personality disorder, schizophrenia, and autism. We believe our model can help identify potential sufferers with mental illness based on their posts. This study further discusses the implication of our proposed model, which can serve as a supplementary tool for monitoring mental health states of individuals who frequently use social media. Social media is a popular space for expressing users' feelings 1,2. Through diverse social media or online social health communities, users often are likely to present their mental problems or illness with anonymity 3. Such online health communities can be a network for expressing sympathy by communicating with others who have similar symptoms 4. In addition, users often try to obtain health information related to their symptoms on social media as an attempt to diagnose themselves 5,6. With this trend, several scholars have analyzed user-generated content on social media for observing users' emotional state or mental illness, including depression, anxiety, or schizophrenia 3,6-10. A recent study collected Twitter posts of users who reportedly had been diagnosed as depression 7 , analyzed the linguistic and emotional characteristics of the collected posts using the Linguistic Inquiry and Word Count (LIWC) 11 , and tracked their social engagement changes on Twitter. Another study attempted to predict users' postpartum depression on Facebook, based on their posts and comments, and used specialized psychometric instruments to evaluate the level of postpartum depression between pre-and post-natal periods 12. In addition, Reece et al. 13 used image data to detect users' depression on social network services. After collecting photos from Instagram uploaded by users, both face detection and colorimetric analysis were applied. To detect users' anxiety disorders, prior research collected user data from Reddit and showed that N-gram language modeling and vector embedding procedures with topic analysis of users' posts are efficient in finding potential users with anxiety disorders 3. Several previous studies revealed that social media data is useful in observing or detecting users' emotions or potential mental problems. This study goes one step further; by collecting various mental-health-related data from social media, we aim at developing a deep learning model that can identify a user's mental disorder, including depression, anxiety, bipolar, borderline personality disorder (BPD), schizophrenia, and autism. To this end, we collected users' posts from Reddit, a popular social media that incl...

A new endonuclease/methyltransferase activity assay method based on graphene oxide (GO) is developed. Substrate DNA is designed to possess a double-stranded part to serve as a nuclease substrate and a single-stranded part for anchoring the DNA to the GO surface via strong noncovalent binding. Nuclease-mediated DNA hydrolysis induces the recovery of fluorescence intensity of the dye attached to the end of the double-stranded DNA region. This GO-based method allows real-time measurement and quantitative assay for endonuclease/methyltransferase activities in short time.